Signaling by the tyrosine kinase Yes promotes liver cancer development
Bibliographic record
Abstract
Most patients with hepatocellular carcinoma (HCC) are diagnosed at a late stage and have few therapeutic options and a poor prognosis. This is due to the lack of clearly defined underlying mechanisms or a dominant oncogene that can be targeted pharmacologically, unlike in other cancer types. Here, we report the identification of a previously uncharacterized oncogenic signaling pathway in HCC that is mediated by the tyrosine kinase Yes. Using genetic and pharmacological interventions in cellular and mouse models of HCC, we showed that Yes activity was necessary for HCC cell proliferation. Transgenic expression of activated Yes in mouse hepatocytes was sufficient to induce liver tumorigenesis. Yes phosphorylated the transcriptional coactivators YAP and TAZ (YAP/TAZ), promoting their nuclear accumulation and transcriptional activity in HCC cells and liver tumors. We also showed that YAP/TAZ were effectors of the Yes-dependent oncogenic transformation of hepatocytes. Src family kinase activation correlated with the tyrosine phosphorylation and nuclear localization of YAP in human HCC and was associated with increased tumor burden in mice. Specifically, high Yes activity predicted shorter overall survival in patients with HCC. Thus, our findings identify Yes as a potential therapeutic target in HCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".